Abstract

A recent meta-analysis concluded that newer antidepressant drugs are
equivalent to or no better than placebos, a conclusion at some variance with
their commonly judged clinical effectiveness. The ‘disconnect’
betweenrandomised controlled trials and clinical practice would benefit from
dissection of contributing factors, and redressing limitations to current
trial procedures.

A recent meta-analytic study of randomised controlled trial (RCT) data by
Kirsch et
al2 effectively
concluded that the new antidepressant drugs are either no better than placebos
or only as effective as placebos, generating ingenuous acceptance in many lay
and medical publications, and dismissal by many clinicians who view
antidepressant drugs as highly effective. The risk is of debating the findings
rather than the constituent processes. This editorial considers why findings
from RCT evidential bases should not be viewed as usefully generalising to
clinical application.

Do RCTs identify differential effectiveness across treatments of
major depression?

Do antidepressant treatments differentiate from placebos in
RCTs?

Moncrieff et
al4 undertook a
meta-analysis comparing tricyclic antidepressants with placebos – and as
only two of the nine studies favoured the drug, the authors argued for similar
meta-analyses of the newer antidepressants. In 2002, Kirsch et
al5 published
such an analysis, examining RCT data for six new antidepressants. Of the 47
data-sets, the antidepressant did not differentiate from placebo in 9, and for
the remaining 38, the drug–placebo difference was a ‘
trivial’ two points. Their more recent
report2 analysed
data of 35 RCTs comparing 5133 participants randomised to medication and 1841
to placebo, with weighted mean improvement in depression severity being 9.6
and 7.8 points respectively, but with baseline depression severity influencing
drug efficacy. The authors concluded that ‘the overall effect of new
generation antidepressant medications is below recommended criteria for
clinical significance’ and that ‘there seems little evidence to
support the prescription of antidepressant medication to any but the most
severely depressed patients, unless alternative treatments have failed to
provide benefit’.

In relation to the latter, it is often unappreciated that the so-called
evidence-supported psychotherapies (i.e. cognitive–behavioural therapy
and interpersonal psychotherapy) also show non-differentiation from plausible
control strategies in similar
meta-analyses.6

Why the disconnect between RCTs and clinical practice?

Such findings allow three possible explanations: the therapies are
ineffective, analyses are inappropriate or limitations to RCT procedures.

The first explanation is relative, not absolute – it is unlikely that
the ‘evidence-based’ antidepressant therapies are always
ineffective. The second explanation (effectively, ‘garbage in, garbage
out’) was well-addressed by Lieberman et
al,7 who
detailed problems from conducting, reporting and evaluating meta-analyses
involving intent-to-treat and last-observation-carried-forward strategies,
differential attrition, drug dosing (flexible v. fixed), participant
sampling and ‘cherry-picking’ rather than including all relevant
studies. The third explanation – that there are substantive limitations
to current procedures for testing antidepressant treatments – is argued
here as the most sustainable.

Contribution of the criterion diagnosis of major depression

Imagine if major dyspnoea was the criterion diagnosis for an RCT comparing
a putatively effective treatment and a placebo. Further assume that study
participants had various respiratory conditions (pneumonia, asthma, pulmonary
embolus). It would be illogical to test a specific treatment (e.g. antibiotic,
bronchodilator, anticoagulant) as if it had universal application as results
would be influenced by the prevalence of the constituent pathological
disorders. A truly effective treatment would have its efficacy diminished or
nullified by low representation of the target condition.

Thus, if major depression is no more than a ‘domain diagnosis’ –
encapsulating differing constituent disorders (variably responsive to
medication or to a psychotherapy) – then the true efficacy of each
treatment modality is at risk of clouding. Viewing major depression as a
unitary entity – as against a non-specific domain diagnosis capturing
heterogeneous expressions of depression – is a starting point for
downstream non-specific results.

Impact of participant definition in RCTs

Most antidepressant drug trials recruit out-patients and effectively
exclude those with melancholic depression – the quintessential ‘
biological’ depressive condition. Also excluded are those with
suicidal ideation, comorbid drug or alcohol problems, anxiety conditions
and/or personality disorders. Individuals are commonly recruited via public
advertising and may be reimbursed, and trial incentives risk rating up those
with less substantive disorders to meet entry criteria. Such criteria risk
recruiting individuals with less severe non-melancholic disorders and showing
little correspondence with depressed patients presenting to psychiatrists.

As detailed by Lieberman et
al,7 early RCTs
of antidepressants were weighted to hospitalised patients and those with the
more biological mood disorders, with drug–placebo differences of 30%. As
recruitment is increasingly weighted to those with milder, briefer and
self-limiting expressions of depression – with Walsh et
al8 quantifying
a 7% per decade increase in RCT responder rates for antidepressant drug and
placebo – the increased spontaneous remission rates compromise detecting
any signal from truly efficacious antidepressant drugs.

Influence of depression severity in RCTs

Horowitz &
Wakefield9 have
detailed the risk of DSM-defined major depression pathologising states of
normal sadness – and the ‘myth of equivalence’ (of equating
symptom-based diagnoses across community and clinical samples).

At some decreasing level of severity, antidepressant drug treatments may
move from being effective to ineffective – as quantified in the recent
meta-analysis2 –
purely reflecting severity or reflecting low prevalence of the more
severe biological conditions more specifically responsive to antidepressant
drugs.

Further, severity-based measures risk being problematic at lower severity
levels. First, some individuals (including those who might benefit from
medication) may not yet be at the nadir of their illness. As a consequence,
the true impact of an intervention might be compromised at that time. Second
is the difficulty of separating state depression from base functioning. In
clinical practice, an optimal target is for the patient to feel ‘back to
normal’. However, ‘normality’ might include (say) some
distractibility, sleep and appetite disturbance – all symptoms that
generate scores on state depression measures. Thus, non-remission status in an
RCT might reflect a truly ineffective treatment, a partially effective
treatment or merely general functioning.

In most RCTs, however, the primary outcome measure is ‘
responder’ status. Baseline inflations for recruitment
purposes,7 together
with individuals’ placebo and spontaneous improvement propensities, risk
regression to the mean confounding responder status. Responder status may be
achieved by true- and false-positive improvers.

Thus, RCTs risk imprecision if outcome is responder status and confounding
by trait functioning if outcome is remission status. Although corrective
analytic strategies (e.g. mixed model repeated measures) have been
suggested,7 these
are rarely adopted.

Any reprised phenomenological model should prioritise psychotic and
melancholic depression as candidate conditions for demonstrating selective and
distinctive response to antidepressant drugs. As reviewed
elsewhere,3 studies
in the 1960s – in which antidepressants differentiated distinctly from
placebos – were weighted to the melancholic depressive subtype, and
generated response rates of 60–70% to broad-spectrum antidepressant
drugs, with placebo rates as low as 10%.

McHugh10 has
argued for four aetiopathic clusters of mental disorders, including clusters
comprising ‘patients with brain diseases’ (e.g. psychotic and
melancholic depressions), weighting causal factors emerging from temperament
or personality level, and conditions provoked by significant life events.

Antidepressant drugs might be superior for those in the first group;
psychotherapies (e.g. cognitive–behavioural therapy) correcting causal
personality factors might be more salient for those in the second group; and
interpersonal psychotherapy and counselling might be more effective for those
in the third group. The argument put here is that no treatment should be
viewed (or trialled) as having universal (or non-specific) application across
heterogeneous disorders. Rather than selecting treatments on such as basis –
or for eclectic reasons – the field would benefit from a model
that specifies treatments weighted to differing biological, psychological and
social factors contributing to depressive patterns.

Conclusions

If we are to argue that antidepressant drugs are evidence based, then we
need to reconcile the reality that the largest referenced databases provide
limited support for that proposition. The meta-analyses by Kirsch et
al2,5
principally analysed data used by pharmaceutical companies to argue the
efficacy of antidepressant drugs (and have them licensed). If we wish to
reject the imputation that antidepressant drugs are little better than
placebos, we need first to recognise limitations of current RCT procedures and
produce better evidence.

The position put here is not to reject the necessity for RCTs to begin to
inform us about efficacy (and safety) of antidepressant drugs, but to argue
that the limited findings should drive concerns about current diagnostic
classifications, RCT procedures (whereby the ‘apples’ assessed in
such studies do not correspond to the ‘oranges’ of clinical
practice), reliance of treatment guidelines on such RCT findings and how the
evidence-based depression treatments have been positioned at the expense of
appropriate explanatory models. Trialling a (drug or non-drug) treatment as if
it had universal (i.e. non-specific) application for a non-specific condition
(e.g. major depression) risks building to non-specific results. The Kirsch
meta-analysis2
informs us that the consequences of such flawed logic have now been realised.
The current foundations lack a firm base, and the meta-analysis has exposed a
fault line, with flawed paradigms and RCT practices generating limited valid
evidence.